5 Motivation and context Semantically Steered Clinical Decision Support Systems Challenges of Clinical Decision Support Systems (CDSS) 1. Computerization of CDSS 2. Timely advice Recommendations provided at the place and time when they are needed Need of Making recommendations available Fast reasoning process of recommendations generation 5/50

6 Motivation and context Semantically Steered Clinical Decision Support Systems Challenges of Clinical Decision Support Systems (CDSS) 1. Computerization of CDSS 2. Timely advice 3. Maintainability and extensibility When new knowledge is fed to the system Easy to maintain Easy to extend Medical knowledge is continuously changing Frequently found new discoveries New techniques proved validity Affordable solution needed New knowledge provided by Medical and scientific community Experience of medical experts 6/50

7 Motivation and context Semantically Steered Clinical Decision Support Systems Challenges of Clinical Decision Support Systems (CDSS) 1. Computerization of CDSS 2. Timely advice 3. Maintainability and extensibility 4. Clinical workflow integration Decision support integrated into the clinical workflow Minimization of time consumption during the introduction of patient data and results. Assist clinicians during all different tasks of their daily duties» Not only during specific activities. 7/50

9 Motivation and context Semantically Steered Clinical Decision Support Systems This work was driven by the hypothesis that semantics and experience-based technologies can enhance CDSS making them successful in real clinical environments. 9/50

10 Motivation and context Semantically Steered Clinical Decision Support Systems How can clinical experience be modeled, acquired and reused in the context of clinical decision making? Is it possible to develop a semantic steered clinical decision support system that allows the handling of the collective experience of a medical organization? 10/50

11 Motivation and context Semantically Steered Clinical Decision Support Systems Objectives To propose a methodology for the recommendations generation process. To propose a methodology for the automatic evolution of a ruleset, based on the acquired decisional events. To present a generic model for clinical tasks in the context of clinical decision making. To present a generic architecture for S-CDSS that fits in the clinical task model. To present a framework for the management of the clinical experience of a medical organization. 11/50

13 Recommendations generation Recommendations generation Recommendations are a set of alternative options Ranked and presented to system users with their proofs Knowledge-based approach needed Reasoning system 13/50

23 Recommendations generation Extended Reflexive Ontologies Reflexive ontology containing also Rules The matching recommendations for each set of individuals Speed up of the reasoning process Previously made rules do not need to be calculated again 23/50

28 Clinical Experience handling Clinical Experience handling Ruleset evolution 1. Rule weight evolution Combine 2 criteria: QUANTITY: number of times a rule matches the conditions QUALITY: number of times when recommendation becomes a final decision 28/50

29 Clinical Experience handling Clinical Experience handling Ruleset evolution 2. Fine tuning of rules (rule conditional query clauses) Activation count: Total amount of times that a query clause is active in a rule that matches conditions Agreement count: Total of times that being a query clause active in a rule that matches conditions, the recommendation = final decision Ratio: Error prone query clauses > Threshold 29/50

30 Clinical Experience handling Clinical Experience handling Ruleset evolution 3. New rule generation Final decision: validate the set of variables that are relevant Include Remove Generate a new rule Antecedent (IF): new set of relevant values Consequent (THEN): final decision 30/50

47 Challenge Conclusions 4 Conclusions Research question and objectives accomplishment We have demostrated that clinical experience can be modeled, acquired and reused in the context of clinical decision making. In order to allow the handling of the colective experience within a medical organization, we have proposed and developed a theoretical framework its specific recommendations associated methodologies practical tools 47/50

48 Challenge Conclusions 4 Conclusions We presented a methodology for the generation of decision recommendations. We presented a methodology for the acquisition and consolidation of decisional events in the system. In particular, we have provided a methodology for the automatic evolution of a ruleset based on the acquired decisional events. The integration of such contributions into a CDSS allowed us to present an innovative architecture for Semantically steered CDSS (S-CDSS). The architecture fits in the Clinical Task Model (CTM), a generic model for clinical tasks which we also presented. We have achieved an operational implementation of such architecture and methodologies in the framework of two case studies: early diagnosis of AD and breast cancer treatment. 48/50

50 Challenge 4 Future Work Future work Short term Design more refined algorithms in order to extract implicit knowledge from the reflexive structure Evaluate the performance of RO in different domains and use cases Mid term Perform a formal evaluation of our architecture in a real clinical environment Long term Study how to develop decision traceability in the clinical domain Develop automatic knowledge retrieval tools to provide knowledge maintenance Integrate the proposed S-CDSS with hospital EHR EHR semantization 50/50

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